A Noninvasive, Electromagnetic, Epidermal Sensing Device for Hemodynamics Monitoring

Dynamic cerebral blood flow assessment based on electromagnetic coupling sensing and image feature analysis

Dynamic assessment of cerebral blood flow (CBF) is crucial for guiding personalized management and treatment strategies, and improving the prognosis of stroke. However, a safe, reliable, and effective method for dynamic CBF evaluation is currently lacking in clinical practice. In this study, we developed a CBF monitoring system utilizing electromagnetic coupling sensing (ECS). This system detects variations in brain conductivity and dielectric constant by identifying the resonant frequency (RF) in an equivalent circuit containing both magnetic induction and electrical coupling. We evaluated the performance of the system using a self-made physical model of blood vessel pulsation to test pulsatile CBF. Additionally, we recruited 29 healthy volunteers to monitor cerebral oxygen (CO), cerebral blood flow velocity (CBFV) data and RF data before and after caffeine consumption. We analyzed RF and CBFV trends during immediate responses to abnormal intracranial blood supply, induced by changes in vascular stiffness, and compared them with CO data. Furthermore, we explored a method of dynamically assessing the overall level of CBF by leveraging image feature analysis. Experimental testing substantiates that this system provides a detection range and depth enhanced by three to four times compared to conventional electromagnetic detection techniques, thereby comprehensively covering the principal intracranial blood supply areas. And the system effectively captures CBF responses under different intravascular pressure stimulations. In healthy volunteers, as cerebral vascular stiffness increases and CO decreases due to caffeine intake, the RF pulsation amplitude diminishes progressively. Upon extraction and selection of image features, widely used machine learning algorithms exhibit commendable performance in classifying overall CBF levels. These results highlight that our proposed methodology, predicated on ECS and image feature analysis, enables the capture of immediate responses of abnormal intracranial blood supply triggered by alterations in vascular stiffness. Moreover, it provides an accurate diagnosis of the overall CBF level under varying physiological conditions.

Meta-Analysis of Pulse Transition Features in Non-Invasive Blood Pressure Estimation Systems: Bridging Physiology and Engineering Perspectives

The pulse transition features (PTFs), including pulse arrival time (PAT) and pulse transition time (PTT), hold significant importance in estimating non-invasive blood pressure (NIBP). However, the literature showcases considerable variations in terms of PTFs' correlation with blood pressure (BP), accuracy in NIBP estimation, and the comprehension of the relationship between PTFs and BP. This inconsistency is exemplified by the wide-ranging correlations reported across studies investigating the same feature. Furthermore, investigations comparing PAT and PTT have yielded conflicting outcomes. Additionally, PTFs have been derived from various bio-signals, capturing distinct characteristic points like the pulse's foot and peak. To address these inconsistencies, this study meticulously reviews a selection of such research endeavors while aligning them with the biological intricacies of blood pressure and the human cardiovascular system (CVS). Each study underwent evaluation, considering the specific signal acquisition locale and the corresponding recording procedure. Moreover, a comprehensive meta-analysis was conducted, yielding multiple conclusions that could significantly enhance the design and accuracy of NIBP systems. Grounded in these dual aspects, the study systematically examines PTFs in correlation with the specific study conditions and the underlying factors influencing the CVS. This approach serves as a valuable resource for researchers aiming to optimize the design of BP recording experiments, bio-signal acquisition systems, and the fine-tuning of feature engineering methodologies, ultimately advancing PTF-based NIBP estimation.

A Voxel Vascular Structure-based Mannequin-like Arm Electromagnetic Model for Radio Frequency Biomedical Sensors

Radio Frequency (RF) sensor is widely used to monitor physiological signals. Generally, RF sensor simulation is mostly done using a layered model, which sometimes cannot model the accurate properties in the real world. A voxel vascular structure-based mannequin-like arm electromagnetic model (VVS-MaM) is proposed to evaluate the RF sensor, which mainly gathers the real physiological signal. This model is built with high-precision Magnetic Resonance Imaging (MRI), and it can finish fast simulation while there is also a voxel-like part in it which means it has the advantages of both the layered model and the real human model. After modelling, both simulation and in-vivo experiments are designed to test this sensor. In the simulation, the simulated standard resonant frequency of the equivalent model is 1.8137 GHz, and the relative error of the VVS-MaM is 0.012 GHz, which is closer to the standard value than the layer model result of 0.049 GHz. Meanwhile, in the in-vivo experiments, an RF sensor based on a composite right/left-handed transmission line (CRLH-TL) and complementary split resonator rings (CSRRs) are fabricated, and the measurements from the real experiments are gathered and stored to compare with that of the simulation. The comparison shows that the relative error of the VVS-MaM (0.08804 GHz)is closer to the in-vivo measurements than that of the layer model (0.09891 GHz), which validates the performance of VVS-MaM.Clinical Relevance-Radio Frequency, magnetic resonance imaging, scattering parameter, composite right/left-handed, complementary split resonator ring.

Analysis and design of holographic magnetic metasurfaces in the very near field for sensing applications at quasi-static regime

In this paper, we present a novel low-frequency sensing solution based on the manipulation of the near-field distribution by employing a passive holographic magnetic metasurface, excited by an active RF coil placed in its reactive region. In particular, the sensing capability is based on the interaction between the magnetic field distribution produced by the radiating system and the magneto-dielectric inhomogeneities eventually present within the material under test. We first start from conceiving the geometrical set-up of the metasurface and its driving RF coil, adopting a low operative frequency (specifically 3 MHz) to consider a quasi-static regime and able to increase the penetration depth within the sample. Afterwards, since the sensing spatial resolution and performance can be modulated by controlling the metasurface properties, the required holographic magnetic field mask, describing the ideal distribution at a specific plane, is designed. Then, the amplitude and phase of currents, flowing in each metasurface unit-cell and required to synthetize the field mask, are determined through an optimization technique. Next, the capacitive loads necessary to accomplish the planned behavior are retrieved, by exploiting the metasurface impedance matrix. Finally, experimental measurements conducted on fabricated prototypes validated the numerical results, confirming the efficacy of the proposed approach to detect inhomogeneities in a medium with a magnetic inclusion in a non-destructive manner. The findings show that holographic magnetic metasurfaces operating in the quasi-static regime can be successfully employed for non-destructive sensing, both in industrial and biomedical fields, despite the extremely low frequencies.

Contact-Free, Passive, Electromagnetic Resonant Sensors for Enclosed Biomedical Applications: A Perspective on Opportunities and Challenges

Inexpensive and accurate tools for monitoring conditions in enclosed environments (through garments, bandages, tissue, etc.) have been a long-standing goal of medicine. Passive resonant sensors are a promising solution for such wearable health sensors as well as off-body diagnostics. They are simple circuits with inherent inductance and capacitance (LC tank) that have a measurable resonant frequency. Changes in local parameters, e.g., permittivity or geometry, effect inductance and capacitance which cause a resonant frequency shift response. This signal transduction has been applied to several biomedical applications such as intracranial pressure, hemodynamics, epidermal hydration, etc. Despite these many promising applications presented in the literature, resonant sensors still do not see widespread adoption in biomedical applications, especially as wearable or embedded sensing devices. This perspective highlights some of the current challenges facing LC resonant sensors in biomedical applications, such as positional sensitivity, and potential strategies that have been developed to overcome them. An outlook on adoption in medicine and health monitoring is presented, and a perspective is given on next steps for research in this field.

Wearable Sensing System for NonInvasive Monitoring of Intracranial BioFluid Shifts in Aerospace Applications

The alteration of the hydrostatic pressure gradient in the human body has been associated with changes in human physiology, including abnormal blood flow, syncope, and visual impairment. The focus of this study was to evaluate changes in the resonant frequency of a wearable electromagnetic resonant skin patch sensor during simulated physiological changes observed in aerospace applications. Simulated microgravity was induced in eight healthy human participants (n = 8), and the implementation of lower body negative pressure (LBNP) countermeasures was induced in four healthy human participants (n = 4). The average shift in resonant frequency was -13.76 ± 6.49 MHz for simulated microgravity with a shift in intracranial pressure (ICP) of 9.53 ± 1.32 mmHg, and a shift of 8.80 ± 5.2097 MHz for LBNP with a shift in ICP of approximately -5.83 ± 2.76 mmHg. The constructed regression model to explain the variance in shifts in ICP using the shifts in resonant frequency (R² = 0.97) resulted in a root mean square error of 1.24. This work demonstrates a strong correlation between sensor signal response and shifts in ICP. Furthermore, this study establishes a foundation for future work integrating wearable sensors with alert systems and countermeasure recommendations for pilots and astronauts.

A Flexible Near-Field Biosensor for Multisite Arterial Blood Flow Detection

Modern wearable devices show promising results in terms of detecting vital bodily signs from the wrist. However, there remains a considerable need for a device that can conform to the human body's variable geometry to accurately detect those vital signs and to understand health better. Flexible radio frequency (RF) resonators are well poised to address this need by providing conformable bio-interfaces suitable for different anatomical locations. In this work, we develop a compact wearable RF biosensor that detects multisite hemodynamic events due to pulsatile blood flow through noninvasive tissue-electromagnetic (EM) field interaction. The sensor consists of a skin patch spiral resonator and a wearable transceiver. During resonance, the resonator establishes a strong capacitive coupling with layered dielectric tissues due to impedance matching. Therefore, any variation in the dielectric properties within the near-field of the coupled system will result in field perturbation. This perturbation also results in RF carrier modulation, transduced via a demodulator in the transceiver unit. The main elements of the transceiver consist of a direct digital synthesizer for RF carrier generation and a demodulator unit comprised of a resistive bridge coupled with an envelope detector, a filter, and an amplifier. In this work, we build and study the sensor at the radial artery, thorax, carotid artery, and supraorbital locations of a healthy human subject, which hold clinical significance in evaluating cardiovascular health. The carrier frequency is tuned at the resonance of the spiral resonator, which is 34.5 ± 1.5 MHz. The resulting transient waveforms from the demodulator indicate the presence of hemodynamic events, i.e., systolic upstroke, systolic peak, dicrotic notch, and diastolic downstroke. The preliminary results also confirm the sensor's ability to detect multisite blood flow events noninvasively on a single wearable platform.

An amplitude-based characteristic parameter extraction algorithm for cerebral edema detection based on electromagnetic induction

Background

Cerebral edema is a common condition secondary to any type of neurological injury. The early diagnosis and monitoring of cerebral edema is of great importance to improve the prognosis. In this article, a flexible conformal electromagnetic two-coil sensor was employed as the electromagnetic induction sensor, associated with a vector network analyzer (VNA) for signal generation and receiving. Measurement of amplitude data over the frequency range of 1–100 MHz is conducted to evaluate the changes in cerebral edema. We proposed an Amplitude-based Characteristic Parameter Extraction (Ab-CPE) algorithm for multi-frequency characteristic analysis over the frequency range of 1–100 MHz and investigated its performance in electromagnetic induction-based cerebral edema detection and distinction of its acute/chronic phase. Fourteen rabbits were enrolled to establish cerebral edema model and the 24 h real-time monitoring experiments were carried out for algorithm verification.

Results

The proposed Ab-CPE algorithm was able to detect cerebral edema with a sensitivity of 94.1% and specificity of 95.4%. Also, in the early stage, it can detect cerebral edema with a sensitivity of 85.0% and specificity of 87.5%. Moreover, the Ab-CPE algorithm was able to distinguish between acute and chronic phase of cerebral edema with a sensitivity of 85.0% and specificity of 91.0%.

Conclusion

The proposed Ab-CPE algorithm is suitable for multi-frequency characteristic analysis. Combined with this algorithm, the electromagnetic induction method has an excellent performance on the detection and monitoring of cerebral edema.